Overview

Dataset statistics

Number of variables24
Number of observations3376
Missing cells4298
Missing cells (%)5.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory633.1 KiB
Average record size in memory192.0 B

Variable types

Categorical5
Numeric19

Alerts

NumberofBuildings is highly overall correlated with OutlierHigh correlation
PropertyGFATotal is highly overall correlated with PropertyGFABuilding(s) and 5 other fieldsHigh correlation
PropertyGFAParking is highly overall correlated with OutlierHigh correlation
PropertyGFABuilding(s) is highly overall correlated with PropertyGFATotal and 5 other fieldsHigh correlation
ENERGYSTARScore is highly overall correlated with SourceEUI(kBtu/sf) and 2 other fieldsHigh correlation
SiteEUI(kBtu/sf) is highly overall correlated with SiteEUIWN(kBtu/sf) and 9 other fieldsHigh correlation
SiteEUIWN(kBtu/sf) is highly overall correlated with SiteEUI(kBtu/sf) and 8 other fieldsHigh correlation
SourceEUI(kBtu/sf) is highly overall correlated with ENERGYSTARScore and 7 other fieldsHigh correlation
SourceEUIWN(kBtu/sf) is highly overall correlated with ENERGYSTARScore and 7 other fieldsHigh correlation
SiteEnergyUse(kBtu) is highly overall correlated with PropertyGFATotal and 11 other fieldsHigh correlation
SiteEnergyUseWN(kBtu) is highly overall correlated with PropertyGFATotal and 11 other fieldsHigh correlation
SteamUse(kBtu) is highly overall correlated with OutlierHigh correlation
Electricity(kBtu) is highly overall correlated with PropertyGFATotal and 8 other fieldsHigh correlation
NaturalGas(kBtu) is highly overall correlated with SiteEUI(kBtu/sf) and 6 other fieldsHigh correlation
TotalGHGEmissions is highly overall correlated with PropertyGFATotal and 9 other fieldsHigh correlation
GHGEmissionsIntensity is highly overall correlated with SiteEUI(kBtu/sf) and 6 other fieldsHigh correlation
BuildingType is highly overall correlated with PrimaryPropertyTypeHigh correlation
PrimaryPropertyType is highly overall correlated with BuildingTypeHigh correlation
Outlier is highly overall correlated with NumberofBuildings and 15 other fieldsHigh correlation
ComplianceStatus is highly imbalanced (83.1%)Imbalance
ENERGYSTARScore has 843 (25.0%) missing valuesMissing
Outlier has 3344 (99.1%) missing valuesMissing
NumberofBuildings is highly skewed (γ1 = 43.39499472)Skewed
PropertyGFATotal is highly skewed (γ1 = 24.12940742)Skewed
PropertyGFABuilding(s) is highly skewed (γ1 = 27.62439064)Skewed
SiteEnergyUse(kBtu) is highly skewed (γ1 = 24.84197927)Skewed
SteamUse(kBtu) is highly skewed (γ1 = 26.72088824)Skewed
Electricity(kBtu) is highly skewed (γ1 = 28.72846389)Skewed
NaturalGas(kBtu) is highly skewed (γ1 = 30.03889028)Skewed
NumberofBuildings has 92 (2.7%) zerosZeros
PropertyGFAParking has 2872 (85.1%) zerosZeros
SourceEUIWN(kBtu/sf) has 36 (1.1%) zerosZeros
SteamUse(kBtu) has 3237 (95.9%) zerosZeros
NaturalGas(kBtu) has 1258 (37.3%) zerosZeros

Reproduction

Analysis started2023-06-09 08:22:14.804463
Analysis finished2023-06-09 08:25:18.087004
Duration3 minutes and 3.28 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

BuildingType
Categorical

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size26.5 KiB
nonresidential
1460 
multifamily lr (1-4)
1018 
multifamily mr (5-9)
580 
multifamily hr (10+)
 
110
sps-district k-12
 
98
Other values (3)
 
110

Length

Max length20
Median length20
Mean length17.167358
Min length6

Characters and Unicode

Total characters57957
Distinct characters30
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rownonresidential
2nd rownonresidential
3rd rownonresidential
4th rownonresidential
5th rownonresidential

Common Values

ValueCountFrequency (%)
nonresidential 1460
43.2%
multifamily lr (1-4) 1018
30.2%
multifamily mr (5-9) 580
 
17.2%
multifamily hr (10+) 110
 
3.3%
sps-district k-12 98
 
2.9%
nonresidential cos 85
 
2.5%
campus 24
 
0.7%
nonresidential wa 1
 
< 0.1%

Length

2023-06-09T10:25:18.456971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-09T10:25:19.321717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
multifamily 1708
24.5%
nonresidential 1546
22.2%
lr 1018
14.6%
1-4 1018
14.6%
mr 580
 
8.3%
5-9 580
 
8.3%
hr 110
 
1.6%
10 110
 
1.6%
sps-district 98
 
1.4%
k-12 98
 
1.4%
Other values (3) 110
 
1.6%

Most occurring characters

ValueCountFrequency (%)
i 6704
 
11.6%
l 5980
 
10.3%
n 4638
 
8.0%
m 4020
 
6.9%
3600
 
6.2%
t 3450
 
6.0%
r 3352
 
5.8%
a 3279
 
5.7%
e 3092
 
5.3%
s 1949
 
3.4%
Other values (20) 17893
30.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 45425
78.4%
Decimal Number 3612
 
6.2%
Space Separator 3600
 
6.2%
Dash Punctuation 1794
 
3.1%
Close Punctuation 1708
 
2.9%
Open Punctuation 1708
 
2.9%
Math Symbol 110
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 6704
14.8%
l 5980
13.2%
n 4638
10.2%
m 4020
8.8%
t 3450
7.6%
r 3352
7.4%
a 3279
7.2%
e 3092
6.8%
s 1949
 
4.3%
u 1732
 
3.8%
Other values (9) 7229
15.9%
Decimal Number
ValueCountFrequency (%)
1 1226
33.9%
4 1018
28.2%
5 580
16.1%
9 580
16.1%
0 110
 
3.0%
2 98
 
2.7%
Space Separator
ValueCountFrequency (%)
3600
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1794
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1708
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1708
100.0%
Math Symbol
ValueCountFrequency (%)
+ 110
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 45425
78.4%
Common 12532
 
21.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 6704
14.8%
l 5980
13.2%
n 4638
10.2%
m 4020
8.8%
t 3450
7.6%
r 3352
7.4%
a 3279
7.2%
e 3092
6.8%
s 1949
 
4.3%
u 1732
 
3.8%
Other values (9) 7229
15.9%
Common
ValueCountFrequency (%)
3600
28.7%
- 1794
14.3%
) 1708
13.6%
( 1708
13.6%
1 1226
 
9.8%
4 1018
 
8.1%
5 580
 
4.6%
9 580
 
4.6%
0 110
 
0.9%
+ 110
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57957
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 6704
 
11.6%
l 5980
 
10.3%
n 4638
 
8.0%
m 4020
 
6.9%
3600
 
6.2%
t 3450
 
6.0%
r 3352
 
5.8%
a 3279
 
5.7%
e 3092
 
5.3%
s 1949
 
3.4%
Other values (20) 17893
30.9%
Distinct24
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size26.5 KiB
low-rise multifamily
987 
mid-rise multifamily
564 
small- and mid-sized office
293 
other
256 
warehouse
187 
Other values (19)
1089 

Length

Max length27
Median length22
Mean length17.189277
Min length5

Characters and Unicode

Total characters58031
Distinct characters29
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhotel
2nd rowhotel
3rd rowhotel
4th rowhotel
5th rowhotel

Common Values

ValueCountFrequency (%)
low-rise multifamily 987
29.2%
mid-rise multifamily 564
16.7%
small- and mid-sized office 293
 
8.7%
other 256
 
7.6%
warehouse 187
 
5.5%
large office 173
 
5.1%
k-12 school 139
 
4.1%
mixed use property 133
 
3.9%
high-rise multifamily 105
 
3.1%
retail store 91
 
2.7%
Other values (14) 448
13.3%

Length

2023-06-09T10:25:20.252090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
multifamily 1656
23.6%
low-rise 987
14.1%
mid-rise 564
 
8.0%
office 508
 
7.2%
small 293
 
4.2%
and 293
 
4.2%
mid-sized 293
 
4.2%
other 256
 
3.6%
warehouse 199
 
2.8%
large 173
 
2.5%
Other values (28) 1794
25.6%

Most occurring characters

ValueCountFrequency (%)
i 7607
13.1%
l 5597
 
9.6%
m 4764
 
8.2%
e 4535
 
7.8%
3640
 
6.3%
r 3355
 
5.8%
s 3179
 
5.5%
a 3045
 
5.2%
o 2881
 
5.0%
f 2811
 
4.8%
Other values (19) 16617
28.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 51664
89.0%
Space Separator 3640
 
6.3%
Dash Punctuation 2409
 
4.2%
Decimal Number 278
 
0.5%
Other Punctuation 40
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 7607
14.7%
l 5597
10.8%
m 4764
9.2%
e 4535
8.8%
r 3355
 
6.5%
s 3179
 
6.2%
a 3045
 
5.9%
o 2881
 
5.6%
f 2811
 
5.4%
t 2796
 
5.4%
Other values (14) 11094
21.5%
Decimal Number
ValueCountFrequency (%)
1 139
50.0%
2 139
50.0%
Space Separator
ValueCountFrequency (%)
3640
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2409
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 51664
89.0%
Common 6367
 
11.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 7607
14.7%
l 5597
10.8%
m 4764
9.2%
e 4535
8.8%
r 3355
 
6.5%
s 3179
 
6.2%
a 3045
 
5.9%
o 2881
 
5.6%
f 2811
 
5.4%
t 2796
 
5.4%
Other values (14) 11094
21.5%
Common
ValueCountFrequency (%)
3640
57.2%
- 2409
37.8%
1 139
 
2.2%
2 139
 
2.2%
/ 40
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58031
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 7607
13.1%
l 5597
 
9.6%
m 4764
 
8.2%
e 4535
 
7.8%
3640
 
6.3%
r 3355
 
5.8%
s 3179
 
5.5%
a 3045
 
5.2%
o 2881
 
5.0%
f 2811
 
4.8%
Other values (19) 16617
28.6%

ZipCode
Real number (ℝ)

Distinct55
Distinct (%)1.6%
Missing16
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean98116.949
Minimum98006
Maximum98272
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2023-06-09T10:25:20.940024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum98006
5-th percentile98101
Q198105
median98115
Q398122
95-th percentile98144
Maximum98272
Range266
Interquartile range (IQR)17

Descriptive statistics

Standard deviation18.615205
Coefficient of variation (CV)0.00018972466
Kurtosis10.492965
Mean98116.949
Median Absolute Deviation (MAD)10
Skewness1.9996622
Sum3.2967295 × 108
Variance346.52584
MonotonicityNot monotonic
2023-06-09T10:25:21.654801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98109 294
 
8.7%
98104 251
 
7.4%
98122 243
 
7.2%
98101 230
 
6.8%
98105 191
 
5.7%
98134 186
 
5.5%
98121 186
 
5.5%
98102 169
 
5.0%
98119 167
 
4.9%
98103 161
 
4.8%
Other values (45) 1282
38.0%
ValueCountFrequency (%)
98006 1
< 0.1%
98011 1
< 0.1%
98012 1
< 0.1%
98013 2
0.1%
98020 1
< 0.1%
98028 1
< 0.1%
98033 1
< 0.1%
98040 1
< 0.1%
98053 1
< 0.1%
98070 1
< 0.1%
ValueCountFrequency (%)
98272 1
 
< 0.1%
98204 1
 
< 0.1%
98199 70
2.1%
98198 1
 
< 0.1%
98195 10
 
0.3%
98191 1
 
< 0.1%
98185 1
 
< 0.1%
98181 1
 
< 0.1%
98178 4
 
0.1%
98177 2
 
0.1%

Neighborhood
Categorical

Distinct14
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size26.5 KiB
downtown
573 
east
453 
magnolia / queen anne
423 
greater duwamish
375 
northeast
280 
Other values (9)
1272 

Length

Max length22
Median length16
Mean length10.11404
Min length4

Characters and Unicode

Total characters34145
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowdowntown
2nd rowdowntown
3rd rowdowntown
4th rowdowntown
5th rowdowntown

Common Values

ValueCountFrequency (%)
downtown 573
17.0%
east 453
13.4%
magnolia / queen anne 423
12.5%
greater duwamish 375
11.1%
northeast 280
8.3%
lake union 251
7.4%
northwest 221
 
6.5%
north 187
 
5.5%
southwest 166
 
4.9%
central 134
 
4.0%
Other values (4) 313
9.3%

Length

2023-06-09T10:25:22.406334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
downtown 573
10.9%
east 453
 
8.6%
magnolia 423
 
8.0%
423
 
8.0%
queen 423
 
8.0%
anne 423
 
8.0%
greater 375
 
7.1%
duwamish 375
 
7.1%
northeast 280
 
5.3%
union 251
 
4.8%
Other values (9) 1273
24.1%

Most occurring characters

ValueCountFrequency (%)
n 4163
12.2%
e 3790
11.1%
a 3498
10.2%
t 3246
9.5%
o 2772
 
8.1%
w 1908
 
5.6%
1896
 
5.6%
s 1852
 
5.4%
r 1791
 
5.2%
h 1326
 
3.9%
Other values (11) 7903
23.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 31826
93.2%
Space Separator 1896
 
5.6%
Other Punctuation 423
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 4163
13.1%
e 3790
11.9%
a 3498
11.0%
t 3246
10.2%
o 2772
8.7%
w 1908
 
6.0%
s 1852
 
5.8%
r 1791
 
5.6%
h 1326
 
4.2%
u 1310
 
4.1%
Other values (9) 6170
19.4%
Space Separator
ValueCountFrequency (%)
1896
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 423
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 31826
93.2%
Common 2319
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 4163
13.1%
e 3790
11.9%
a 3498
11.0%
t 3246
10.2%
o 2772
8.7%
w 1908
 
6.0%
s 1852
 
5.8%
r 1791
 
5.6%
h 1326
 
4.2%
u 1310
 
4.1%
Other values (9) 6170
19.4%
Common
ValueCountFrequency (%)
1896
81.8%
/ 423
 
18.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34145
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 4163
12.2%
e 3790
11.1%
a 3498
10.2%
t 3246
9.5%
o 2772
 
8.1%
w 1908
 
5.6%
1896
 
5.6%
s 1852
 
5.4%
r 1791
 
5.2%
h 1326
 
3.9%
Other values (11) 7903
23.1%

YearBuilt
Real number (ℝ)

Distinct113
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1968.5732
Minimum1900
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2023-06-09T10:25:23.140849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1908
Q11948
median1975
Q31997
95-th percentile2012
Maximum2015
Range115
Interquartile range (IQR)49

Descriptive statistics

Standard deviation33.088156
Coefficient of variation (CV)0.016808192
Kurtosis-0.87134177
Mean1968.5732
Median Absolute Deviation (MAD)24
Skewness-0.53944456
Sum6645903
Variance1094.8261
MonotonicityNot monotonic
2023-06-09T10:25:23.927065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000 72
 
2.1%
2014 67
 
2.0%
1989 67
 
2.0%
2008 66
 
2.0%
1988 64
 
1.9%
1999 64
 
1.9%
1968 63
 
1.9%
1990 60
 
1.8%
2001 60
 
1.8%
2002 59
 
1.7%
Other values (103) 2734
81.0%
ValueCountFrequency (%)
1900 55
1.6%
1901 8
 
0.2%
1902 11
 
0.3%
1903 4
 
0.1%
1904 15
 
0.4%
1905 9
 
0.3%
1906 19
 
0.6%
1907 31
0.9%
1908 27
0.8%
1909 32
0.9%
ValueCountFrequency (%)
2015 37
1.1%
2014 67
2.0%
2013 51
1.5%
2012 35
1.0%
2011 15
 
0.4%
2010 24
 
0.7%
2009 41
1.2%
2008 66
2.0%
2007 42
1.2%
2006 45
1.3%

NumberofBuildings
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct17
Distinct (%)0.5%
Missing8
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1.1068884
Minimum0
Maximum111
Zeros92
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2023-06-09T10:25:24.474182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum111
Range111
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.1084018
Coefficient of variation (CV)1.9048007
Kurtosis2205.2962
Mean1.1068884
Median Absolute Deviation (MAD)0
Skewness43.394995
Sum3728
Variance4.4453579
MonotonicityNot monotonic
2023-06-09T10:25:24.921878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 3175
94.0%
0 92
 
2.7%
2 37
 
1.1%
3 22
 
0.7%
4 12
 
0.4%
5 10
 
0.3%
6 5
 
0.1%
8 3
 
0.1%
10 2
 
0.1%
9 2
 
0.1%
Other values (7) 8
 
0.2%
(Missing) 8
 
0.2%
ValueCountFrequency (%)
0 92
 
2.7%
1 3175
94.0%
2 37
 
1.1%
3 22
 
0.7%
4 12
 
0.4%
5 10
 
0.3%
6 5
 
0.1%
7 1
 
< 0.1%
8 3
 
0.1%
9 2
 
0.1%
ValueCountFrequency (%)
111 1
 
< 0.1%
27 1
 
< 0.1%
23 1
 
< 0.1%
16 1
 
< 0.1%
14 2
0.1%
11 1
 
< 0.1%
10 2
0.1%
9 2
0.1%
8 3
0.1%
7 1
 
< 0.1%

NumberofFloors
Real number (ℝ)

Distinct50
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7091232
Minimum0
Maximum99
Zeros16
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2023-06-09T10:25:25.467295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q35
95-th percentile12
Maximum99
Range99
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.4944648
Coefficient of variation (CV)1.1667702
Kurtosis55.950645
Mean4.7091232
Median Absolute Deviation (MAD)2
Skewness5.9223397
Sum15898
Variance30.189143
MonotonicityNot monotonic
2023-06-09T10:25:26.119086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 692
20.5%
3 692
20.5%
1 466
13.8%
2 439
13.0%
6 306
9.1%
5 295
8.7%
7 148
 
4.4%
8 64
 
1.9%
10 32
 
0.9%
11 32
 
0.9%
Other values (40) 210
 
6.2%
ValueCountFrequency (%)
0 16
 
0.5%
1 466
13.8%
2 439
13.0%
3 692
20.5%
4 692
20.5%
5 295
8.7%
6 306
9.1%
7 148
 
4.4%
8 64
 
1.9%
9 18
 
0.5%
ValueCountFrequency (%)
99 1
 
< 0.1%
76 1
 
< 0.1%
63 1
 
< 0.1%
56 1
 
< 0.1%
55 1
 
< 0.1%
49 1
 
< 0.1%
47 1
 
< 0.1%
46 1
 
< 0.1%
42 6
0.2%
41 3
0.1%

PropertyGFATotal
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3195
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94833.537
Minimum11285
Maximum9320156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2023-06-09T10:25:26.651753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11285
5-th percentile21291.5
Q128487
median44175
Q390992
95-th percentile320096
Maximum9320156
Range9308871
Interquartile range (IQR)62505

Descriptive statistics

Standard deviation218837.61
Coefficient of variation (CV)2.3075972
Kurtosis946.23949
Mean94833.537
Median Absolute Deviation (MAD)19739.5
Skewness24.129407
Sum3.2015802 × 108
Variance4.7889898 × 1010
MonotonicityNot monotonic
2023-06-09T10:25:27.213651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36000 9
 
0.3%
25920 8
 
0.2%
28800 7
 
0.2%
21600 7
 
0.2%
24000 6
 
0.2%
22320 4
 
0.1%
30720 4
 
0.1%
30240 4
 
0.1%
43380 3
 
0.1%
31900 3
 
0.1%
Other values (3185) 3321
98.4%
ValueCountFrequency (%)
11285 1
< 0.1%
11685 1
< 0.1%
11968 1
< 0.1%
12294 1
< 0.1%
12769 1
< 0.1%
13157 1
< 0.1%
13661 1
< 0.1%
14101 1
< 0.1%
15398 1
< 0.1%
16000 1
< 0.1%
ValueCountFrequency (%)
9320156 1
< 0.1%
2200000 1
< 0.1%
1952220 1
< 0.1%
1765970 1
< 0.1%
1605578 1
< 0.1%
1592914 1
< 0.1%
1585960 1
< 0.1%
1536606 1
< 0.1%
1400000 2
0.1%
1380959 1
< 0.1%

PropertyGFAParking
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct496
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8001.5261
Minimum0
Maximum512608
Zeros2872
Zeros (%)85.1%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2023-06-09T10:25:27.733263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile46400.75
Maximum512608
Range512608
Interquartile range (IQR)0

Descriptive statistics

Standard deviation32326.724
Coefficient of variation (CV)4.0400698
Kurtosis58.974892
Mean8001.5261
Median Absolute Deviation (MAD)0
Skewness6.6511908
Sum27013152
Variance1.0450171 × 109
MonotonicityNot monotonic
2023-06-09T10:25:28.337750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2872
85.1%
13320 3
 
0.1%
10800 2
 
0.1%
20416 2
 
0.1%
30000 2
 
0.1%
22000 2
 
0.1%
100176 2
 
0.1%
25800 2
 
0.1%
12960 2
 
0.1%
756 1
 
< 0.1%
Other values (486) 486
 
14.4%
ValueCountFrequency (%)
0 2872
85.1%
38 1
 
< 0.1%
260 1
 
< 0.1%
415 1
 
< 0.1%
604 1
 
< 0.1%
756 1
 
< 0.1%
800 1
 
< 0.1%
919 1
 
< 0.1%
1263 1
 
< 0.1%
1392 1
 
< 0.1%
ValueCountFrequency (%)
512608 1
< 0.1%
407795 1
< 0.1%
389860 1
< 0.1%
368980 1
< 0.1%
335109 1
< 0.1%
327680 1
< 0.1%
319400 1
< 0.1%
303707 1
< 0.1%
285688 1
< 0.1%
285000 1
< 0.1%

PropertyGFABuilding(s)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3193
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86832.011
Minimum3636
Maximum9320156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2023-06-09T10:25:28.876975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3636
5-th percentile21021
Q127756
median43216
Q384276.25
95-th percentile282658.5
Maximum9320156
Range9316520
Interquartile range (IQR)56520.25

Descriptive statistics

Standard deviation207939.81
Coefficient of variation (CV)2.3947368
Kurtosis1161.3603
Mean86832.011
Median Absolute Deviation (MAD)18958.5
Skewness27.624391
Sum2.9314487 × 108
Variance4.3238965 × 1010
MonotonicityNot monotonic
2023-06-09T10:25:29.480168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36000 9
 
0.3%
25920 8
 
0.2%
21600 7
 
0.2%
28800 7
 
0.2%
24000 6
 
0.2%
30240 4
 
0.1%
22320 4
 
0.1%
30720 4
 
0.1%
25800 3
 
0.1%
31900 3
 
0.1%
Other values (3183) 3321
98.4%
ValueCountFrequency (%)
3636 1
< 0.1%
10925 1
< 0.1%
11285 1
< 0.1%
11440 1
< 0.1%
11685 1
< 0.1%
11968 1
< 0.1%
12294 1
< 0.1%
12769 1
< 0.1%
12806 1
< 0.1%
13157 1
< 0.1%
ValueCountFrequency (%)
9320156 1
< 0.1%
2200000 1
< 0.1%
1765970 1
< 0.1%
1632820 1
< 0.1%
1592914 1
< 0.1%
1400000 1
< 0.1%
1380959 1
< 0.1%
1323055 1
< 0.1%
1258280 1
< 0.1%
1215718 1
< 0.1%

ENERGYSTARScore
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct100
Distinct (%)3.9%
Missing843
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean67.918674
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2023-06-09T10:25:30.106472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q153
median75
Q390
95-th percentile99
Maximum100
Range99
Interquartile range (IQR)37

Descriptive statistics

Standard deviation26.873271
Coefficient of variation (CV)0.39566837
Kurtosis-0.21956688
Mean67.918674
Median Absolute Deviation (MAD)17
Skewness-0.85946132
Sum172038
Variance722.17269
MonotonicityNot monotonic
2023-06-09T10:25:30.705354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 109
 
3.2%
98 72
 
2.1%
96 64
 
1.9%
89 58
 
1.7%
93 57
 
1.7%
92 53
 
1.6%
95 51
 
1.5%
94 49
 
1.5%
91 49
 
1.5%
99 49
 
1.5%
Other values (90) 1922
56.9%
(Missing) 843
25.0%
ValueCountFrequency (%)
1 36
1.1%
2 10
 
0.3%
3 13
 
0.4%
4 5
 
0.1%
5 10
 
0.3%
6 8
 
0.2%
7 10
 
0.3%
8 10
 
0.3%
9 5
 
0.1%
10 10
 
0.3%
ValueCountFrequency (%)
100 109
3.2%
99 49
1.5%
98 72
2.1%
97 48
1.4%
96 64
1.9%
95 51
1.5%
94 49
1.5%
93 57
1.7%
92 53
1.6%
91 49
1.5%

SiteEUI(kBtu/sf)
Real number (ℝ)

Distinct1085
Distinct (%)32.2%
Missing7
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean54.732116
Minimum0
Maximum834.40002
Zeros16
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2023-06-09T10:25:31.315092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16.98
Q127.9
median38.599998
Q360.400002
95-th percentile146.9
Maximum834.40002
Range834.40002
Interquartile range (IQR)32.500002

Descriptive statistics

Standard deviation56.273124
Coefficient of variation (CV)1.0281555
Kurtosis39.994568
Mean54.732116
Median Absolute Deviation (MAD)13.5
Skewness4.9818857
Sum184392.5
Variance3166.6645
MonotonicityNot monotonic
2023-06-09T10:25:31.845667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.70000076 17
 
0.5%
28.79999924 17
 
0.5%
0 16
 
0.5%
24.20000076 16
 
0.5%
32 15
 
0.4%
26.39999962 14
 
0.4%
31.70000076 14
 
0.4%
28.89999962 14
 
0.4%
29.60000038 13
 
0.4%
22.79999924 13
 
0.4%
Other values (1075) 3220
95.4%
ValueCountFrequency (%)
0 16
0.5%
0.400000006 1
 
< 0.1%
0.699999988 1
 
< 0.1%
1 1
 
< 0.1%
1.399999976 1
 
< 0.1%
1.799999952 2
 
0.1%
2.099999905 1
 
< 0.1%
2.299999952 1
 
< 0.1%
3 1
 
< 0.1%
3.200000048 1
 
< 0.1%
ValueCountFrequency (%)
834.4000244 1
< 0.1%
707.2999878 1
< 0.1%
696.7000122 1
< 0.1%
694.7000122 1
< 0.1%
639.7000122 1
< 0.1%
593.5999756 1
< 0.1%
465.5 1
< 0.1%
456.6000061 1
< 0.1%
438.2000122 1
< 0.1%
412.7000122 1
< 0.1%

SiteEUIWN(kBtu/sf)
Real number (ℝ)

Distinct1105
Distinct (%)32.8%
Missing6
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean57.033798
Minimum0
Maximum834.40002
Zeros29
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2023-06-09T10:25:32.503053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17.4
Q129.4
median40.900002
Q364.275002
95-th percentile149.155
Maximum834.40002
Range834.40002
Interquartile range (IQR)34.875002

Descriptive statistics

Standard deviation57.16333
Coefficient of variation (CV)1.0022711
Kurtosis37.639503
Mean57.033798
Median Absolute Deviation (MAD)14.300001
Skewness4.8275177
Sum192203.9
Variance3267.6463
MonotonicityNot monotonic
2023-06-09T10:25:33.158290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 29
 
0.9%
29.5 17
 
0.5%
30.79999924 15
 
0.4%
27.89999962 14
 
0.4%
30.20000076 14
 
0.4%
31.60000038 14
 
0.4%
29 14
 
0.4%
32.20000076 14
 
0.4%
33.59999847 13
 
0.4%
31.39999962 13
 
0.4%
Other values (1095) 3213
95.2%
ValueCountFrequency (%)
0 29
0.9%
0.400000006 1
 
< 0.1%
0.699999988 1
 
< 0.1%
1 1
 
< 0.1%
1.5 1
 
< 0.1%
1.799999952 2
 
0.1%
2.099999905 1
 
< 0.1%
2.299999952 1
 
< 0.1%
3 1
 
< 0.1%
3.200000048 1
 
< 0.1%
ValueCountFrequency (%)
834.4000244 1
< 0.1%
707.2999878 1
< 0.1%
694.7000122 1
< 0.1%
693.0999756 1
< 0.1%
639.7999878 1
< 0.1%
593.5999756 1
< 0.1%
468.7000122 1
< 0.1%
467 1
< 0.1%
460.1000061 1
< 0.1%
426.6000061 1
< 0.1%

SourceEUI(kBtu/sf)
Real number (ℝ)

Distinct1648
Distinct (%)48.9%
Missing9
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean134.23285
Minimum0
Maximum2620
Zeros24
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2023-06-09T10:25:33.668070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile37.859999
Q174.699997
median96.199997
Q3143.89999
95-th percentile351.67001
Maximum2620
Range2620
Interquartile range (IQR)69.199997

Descriptive statistics

Standard deviation139.28755
Coefficient of variation (CV)1.0376562
Kurtosis77.664778
Mean134.23285
Median Absolute Deviation (MAD)27.799995
Skewness6.5950437
Sum451962
Variance19401.023
MonotonicityNot monotonic
2023-06-09T10:25:34.321803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 24
 
0.7%
83.69999695 9
 
0.3%
68.09999847 9
 
0.3%
90.5 8
 
0.2%
73.09999847 8
 
0.2%
78.59999847 8
 
0.2%
87.69999695 8
 
0.2%
94.09999847 8
 
0.2%
95 8
 
0.2%
69.69999695 8
 
0.2%
Other values (1638) 3269
96.8%
(Missing) 9
 
0.3%
ValueCountFrequency (%)
0 24
0.7%
1.100000024 1
 
< 0.1%
2 1
 
< 0.1%
2.099999905 1
 
< 0.1%
3 1
 
< 0.1%
4.300000191 1
 
< 0.1%
4.5 1
 
< 0.1%
5.800000191 2
 
0.1%
6.400000095 1
 
< 0.1%
6.599999905 2
 
0.1%
ValueCountFrequency (%)
2620 1
< 0.1%
2217.800049 1
< 0.1%
2181.300049 1
< 0.1%
2007.900024 1
< 0.1%
1527.300049 1
< 0.1%
1206.699951 1
< 0.1%
1150.300049 1
< 0.1%
1026.599976 1
< 0.1%
978.9000244 1
< 0.1%
962.0999756 1
< 0.1%

SourceEUIWN(kBtu/sf)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1694
Distinct (%)50.3%
Missing9
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean137.78393
Minimum-2.0999999
Maximum2620
Zeros36
Zeros (%)1.1%
Negative1
Negative (%)< 0.1%
Memory size26.5 KiB
2023-06-09T10:25:34.842270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-2.0999999
5-th percentile37.700001
Q178.400002
median101.1
Q3148.35
95-th percentile353.86
Maximum2620
Range2622.1
Interquartile range (IQR)69.949997

Descriptive statistics

Standard deviation139.10981
Coefficient of variation (CV)1.0096229
Kurtosis77.441862
Mean137.78393
Median Absolute Deviation (MAD)28.5
Skewness6.5696884
Sum463918.5
Variance19351.538
MonotonicityNot monotonic
2023-06-09T10:25:35.484032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36
 
1.1%
73.59999847 9
 
0.3%
87.30000305 9
 
0.3%
93.59999847 8
 
0.2%
75.5 8
 
0.2%
102.4000015 8
 
0.2%
98.90000153 8
 
0.2%
83.5 8
 
0.2%
84.90000153 8
 
0.2%
104.5999985 8
 
0.2%
Other values (1684) 3257
96.5%
(Missing) 9
 
0.3%
ValueCountFrequency (%)
-2.099999905 1
 
< 0.1%
0 36
1.1%
1.100000024 1
 
< 0.1%
2.200000048 1
 
< 0.1%
3 1
 
< 0.1%
4.599999905 1
 
< 0.1%
5.400000095 1
 
< 0.1%
5.800000191 2
 
0.1%
6.400000095 1
 
< 0.1%
6.599999905 1
 
< 0.1%
ValueCountFrequency (%)
2620 1
< 0.1%
2217.800049 1
< 0.1%
2181.300049 1
< 0.1%
2008 1
< 0.1%
1527.300049 1
< 0.1%
1195.099976 1
< 0.1%
1138.400024 1
< 0.1%
1001 1
< 0.1%
978.9000244 1
< 0.1%
954 1
< 0.1%

SiteEnergyUse(kBtu)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3354
Distinct (%)99.5%
Missing5
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean5403667.3
Minimum0
Maximum8.7392371 × 108
Zeros18
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2023-06-09T10:25:36.063101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile491819.95
Q1925128.59
median1803753.2
Q34222455.2
95-th percentile18161625
Maximum8.7392371 × 108
Range8.7392371 × 108
Interquartile range (IQR)3297326.7

Descriptive statistics

Standard deviation21610629
Coefficient of variation (CV)3.9992523
Kurtosis858.61848
Mean5403667.3
Median Absolute Deviation (MAD)1074356.1
Skewness24.841979
Sum1.8215762 × 1010
Variance4.6701927 × 1014
MonotonicityNot monotonic
2023-06-09T10:25:36.864854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18
 
0.5%
586358.875 1
 
< 0.1%
12213423 1
 
< 0.1%
6714540 1
 
< 0.1%
4653535 1
 
< 0.1%
8163413 1
 
< 0.1%
415364.5938 1
 
< 0.1%
561473.875 1
 
< 0.1%
905750.375 1
 
< 0.1%
2253647.75 1
 
< 0.1%
Other values (3344) 3344
99.1%
(Missing) 5
 
0.1%
ValueCountFrequency (%)
0 18
0.5%
13409 1
 
< 0.1%
16808.90039 1
 
< 0.1%
24105.5 1
 
< 0.1%
44293.5 1
 
< 0.1%
57133.19922 1
 
< 0.1%
72370.39844 1
 
< 0.1%
79711.79688 1
 
< 0.1%
90558.70313 1
 
< 0.1%
97690.39844 1
 
< 0.1%
ValueCountFrequency (%)
873923712 1
< 0.1%
448385312 1
< 0.1%
293090784 1
< 0.1%
291614432 1
< 0.1%
274682208 1
< 0.1%
253832464 1
< 0.1%
163945984 1
< 0.1%
143423024 1
< 0.1%
131373880 1
< 0.1%
114648520 1
< 0.1%

SiteEnergyUseWN(kBtu)
Real number (ℝ)

Distinct3341
Distinct (%)99.1%
Missing6
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean5276725.7
Minimum0
Maximum4.7161386 × 108
Zeros29
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2023-06-09T10:25:37.643557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile503320.81
Q1970182.23
median1904452
Q34381429.1
95-th percentile18203297
Maximum4.7161386 × 108
Range4.7161386 × 108
Interquartile range (IQR)3411246.9

Descriptive statistics

Standard deviation15938786
Coefficient of variation (CV)3.0205827
Kurtosis334.50502
Mean5276725.7
Median Absolute Deviation (MAD)1130097.2
Skewness15.269067
Sum1.7782566 × 1010
Variance2.5404491 × 1014
MonotonicityNot monotonic
2023-06-09T10:25:38.367171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 29
 
0.9%
2127889.25 2
 
0.1%
963968.1875 1
 
< 0.1%
150167.7969 1
 
< 0.1%
1386445.375 1
 
< 0.1%
1519845.5 1
 
< 0.1%
455648.9063 1
 
< 0.1%
13089102 1
 
< 0.1%
6739209 1
 
< 0.1%
4653535 1
 
< 0.1%
Other values (3331) 3331
98.7%
(Missing) 6
 
0.2%
ValueCountFrequency (%)
0 29
0.9%
13409 1
 
< 0.1%
17260 1
 
< 0.1%
24105.5 1
 
< 0.1%
44293.5 1
 
< 0.1%
58114.19922 1
 
< 0.1%
72370.39844 1
 
< 0.1%
79967.89844 1
 
< 0.1%
90558.70313 1
 
< 0.1%
98862.89844 1
 
< 0.1%
ValueCountFrequency (%)
471613856 1
< 0.1%
296671744 1
< 0.1%
295929888 1
< 0.1%
274725984 1
< 0.1%
257764208 1
< 0.1%
167207104 1
< 0.1%
147299056 1
< 0.1%
137106112 1
< 0.1%
123205560 1
< 0.1%
103985264 1
< 0.1%

SteamUse(kBtu)
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct131
Distinct (%)3.9%
Missing9
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean274595.9
Minimum0
Maximum1.3494346 × 108
Zeros3237
Zeros (%)95.9%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2023-06-09T10:25:39.022843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1.3494346 × 108
Range1.3494346 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3912173.4
Coefficient of variation (CV)14.247021
Kurtosis804.86421
Mean274595.9
Median Absolute Deviation (MAD)0
Skewness26.720888
Sum9.2456439 × 108
Variance1.5305101 × 1013
MonotonicityNot monotonic
2023-06-09T10:25:39.776377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3237
95.9%
12508892 1
 
< 0.1%
882630.6875 1
 
< 0.1%
3578548.25 1
 
< 0.1%
1158118.25 1
 
< 0.1%
1165335.75 1
 
< 0.1%
2796766 1
 
< 0.1%
2003882 1
 
< 0.1%
1472483.375 1
 
< 0.1%
230989.2969 1
 
< 0.1%
Other values (121) 121
 
3.6%
(Missing) 9
 
0.3%
ValueCountFrequency (%)
0 3237
95.9%
21230.80078 1
 
< 0.1%
137900 1
 
< 0.1%
151742.5 1
 
< 0.1%
166488.4063 1
 
< 0.1%
175780 1
 
< 0.1%
180731.7969 1
 
< 0.1%
204650 1
 
< 0.1%
230989.2969 1
 
< 0.1%
266262 1
 
< 0.1%
ValueCountFrequency (%)
134943456 1
< 0.1%
122575032 1
< 0.1%
84985240 1
< 0.1%
73885472 1
< 0.1%
31030194 1
< 0.1%
28438884 1
< 0.1%
21566554 1
< 0.1%
19654762 1
< 0.1%
18547858 1
< 0.1%
17548416 1
< 0.1%

Electricity(kBtu)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3351
Distinct (%)99.5%
Missing9
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean3707612.2
Minimum-115417
Maximum6.5707439 × 108
Zeros14
Zeros (%)0.4%
Negative1
Negative (%)< 0.1%
Memory size26.5 KiB
2023-06-09T10:25:40.457611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-115417
5-th percentile247969.2
Q1639487
median1177583
Q32829632.5
95-th percentile13457587
Maximum6.5707439 × 108
Range6.5718981 × 108
Interquartile range (IQR)2190145.5

Descriptive statistics

Standard deviation14850656
Coefficient of variation (CV)4.0054503
Kurtosis1157.4989
Mean3707612.2
Median Absolute Deviation (MAD)684776
Skewness28.728464
Sum1.248353 × 1010
Variance2.2054199 × 1014
MonotonicityNot monotonic
2023-06-09T10:25:40.996600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14
 
0.4%
815508 2
 
0.1%
804194 2
 
0.1%
1084475 2
 
0.1%
3946027 1
 
< 0.1%
166749 1
 
< 0.1%
138179 1
 
< 0.1%
1386445 1
 
< 0.1%
564235 1
 
< 0.1%
421390 1
 
< 0.1%
Other values (3341) 3341
99.0%
(Missing) 9
 
0.3%
ValueCountFrequency (%)
-115417 1
 
< 0.1%
0 14
0.4%
3 1
 
< 0.1%
6138 1
 
< 0.1%
11370 1
 
< 0.1%
13409 1
 
< 0.1%
16765 1
 
< 0.1%
16809 1
 
< 0.1%
24105 1
 
< 0.1%
26365 1
 
< 0.1%
ValueCountFrequency (%)
657074389 1
< 0.1%
274532495 1
< 0.1%
168683602 1
< 0.1%
150476283 1
< 0.1%
139354828 1
< 0.1%
115641210 1
< 0.1%
90060497 1
< 0.1%
87851862 1
< 0.1%
74917352 1
< 0.1%
68636822 1
< 0.1%

NaturalGas(kBtu)
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct2109
Distinct (%)62.6%
Missing9
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean1368504.5
Minimum0
Maximum2.97909 × 108
Zeros1258
Zeros (%)37.3%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2023-06-09T10:25:41.571308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median323754
Q31189033.5
95-th percentile4902372.3
Maximum2.97909 × 108
Range2.97909 × 108
Interquartile range (IQR)1189033.5

Descriptive statistics

Standard deviation6709780.8
Coefficient of variation (CV)4.9030022
Kurtosis1201.0324
Mean1368504.5
Median Absolute Deviation (MAD)323754
Skewness30.03889
Sum4.6077548 × 109
Variance4.5021159 × 1013
MonotonicityNot monotonic
2023-06-09T10:25:42.084701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1258
37.3%
226846 2
 
0.1%
1276453 1
 
< 0.1%
248616 1
 
< 0.1%
11989 1
 
< 0.1%
767235 1
 
< 0.1%
7307702 1
 
< 0.1%
4693677 1
 
< 0.1%
5245042 1
 
< 0.1%
424865 1
 
< 0.1%
Other values (2099) 2099
62.2%
(Missing) 9
 
0.3%
ValueCountFrequency (%)
0 1258
37.3%
33 1
 
< 0.1%
153 1
 
< 0.1%
220 1
 
< 0.1%
332 1
 
< 0.1%
376 1
 
< 0.1%
708 1
 
< 0.1%
764 1
 
< 0.1%
883 1
 
< 0.1%
947 1
 
< 0.1%
ValueCountFrequency (%)
297909000 1
< 0.1%
138191238 1
< 0.1%
84668094 1
< 0.1%
67990538 1
< 0.1%
66746425 1
< 0.1%
56096612 1
< 0.1%
54671394 1
< 0.1%
52975694 1
< 0.1%
34685331 1
< 0.1%
32853512 1
< 0.1%

ComplianceStatus
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.5 KiB
compliant
3211 
error - correct default data
 
113
non-compliant
 
37
missing data
 
15

Length

Max length28
Median length9
Mean length9.693128
Min length9

Characters and Unicode

Total characters32724
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcompliant
2nd rowcompliant
3rd rowcompliant
4th rowcompliant
5th rowcompliant

Common Values

ValueCountFrequency (%)
compliant 3211
95.1%
error - correct default data 113
 
3.3%
non-compliant 37
 
1.1%
missing data 15
 
0.4%

Length

2023-06-09T10:25:42.632870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-09T10:25:43.080389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
compliant 3211
83.6%
data 128
 
3.3%
error 113
 
2.9%
113
 
2.9%
correct 113
 
2.9%
default 113
 
2.9%
non-compliant 37
 
1.0%
missing 15
 
0.4%

Most occurring characters

ValueCountFrequency (%)
a 3617
11.1%
t 3602
11.0%
o 3511
10.7%
c 3474
10.6%
l 3361
10.3%
n 3337
10.2%
i 3278
10.0%
m 3263
10.0%
p 3248
9.9%
r 565
 
1.7%
Other values (8) 1468
4.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 32107
98.1%
Space Separator 467
 
1.4%
Dash Punctuation 150
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3617
11.3%
t 3602
11.2%
o 3511
10.9%
c 3474
10.8%
l 3361
10.5%
n 3337
10.4%
i 3278
10.2%
m 3263
10.2%
p 3248
10.1%
r 565
 
1.8%
Other values (6) 851
 
2.7%
Space Separator
ValueCountFrequency (%)
467
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 150
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 32107
98.1%
Common 617
 
1.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3617
11.3%
t 3602
11.2%
o 3511
10.9%
c 3474
10.8%
l 3361
10.5%
n 3337
10.4%
i 3278
10.2%
m 3263
10.2%
p 3248
10.1%
r 565
 
1.8%
Other values (6) 851
 
2.7%
Common
ValueCountFrequency (%)
467
75.7%
- 150
 
24.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3617
11.1%
t 3602
11.0%
o 3511
10.7%
c 3474
10.6%
l 3361
10.3%
n 3337
10.2%
i 3278
10.0%
m 3263
10.0%
p 3248
9.9%
r 565
 
1.7%
Other values (8) 1468
4.5%

Outlier
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)6.2%
Missing3344
Missing (%)99.1%
Memory size26.5 KiB
low outlier
23 
high outlier

Length

Max length12
Median length11
Mean length11.28125
Min length11

Characters and Unicode

Total characters361
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhigh outlier
2nd rowlow outlier
3rd rowlow outlier
4th rowhigh outlier
5th rowlow outlier

Common Values

ValueCountFrequency (%)
low outlier 23
 
0.7%
high outlier 9
 
0.3%
(Missing) 3344
99.1%

Length

2023-06-09T10:25:43.468091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-09T10:25:43.918313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
outlier 32
50.0%
low 23
35.9%
high 9
 
14.1%

Most occurring characters

ValueCountFrequency (%)
l 55
15.2%
o 55
15.2%
i 41
11.4%
32
8.9%
u 32
8.9%
t 32
8.9%
e 32
8.9%
r 32
8.9%
w 23
6.4%
h 18
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 329
91.1%
Space Separator 32
 
8.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 55
16.7%
o 55
16.7%
i 41
12.5%
u 32
9.7%
t 32
9.7%
e 32
9.7%
r 32
9.7%
w 23
7.0%
h 18
 
5.5%
g 9
 
2.7%
Space Separator
ValueCountFrequency (%)
32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 329
91.1%
Common 32
 
8.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 55
16.7%
o 55
16.7%
i 41
12.5%
u 32
9.7%
t 32
9.7%
e 32
9.7%
r 32
9.7%
w 23
7.0%
h 18
 
5.5%
g 9
 
2.7%
Common
ValueCountFrequency (%)
32
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 361
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 55
15.2%
o 55
15.2%
i 41
11.4%
32
8.9%
u 32
8.9%
t 32
8.9%
e 32
8.9%
r 32
8.9%
w 23
6.4%
h 18
 
5.0%

TotalGHGEmissions
Real number (ℝ)

Distinct2818
Distinct (%)83.7%
Missing9
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean119.72397
Minimum-0.8
Maximum16870.98
Zeros9
Zeros (%)0.3%
Negative1
Negative (%)< 0.1%
Memory size26.5 KiB
2023-06-09T10:25:44.298844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.8
5-th percentile3.78
Q19.495
median33.92
Q393.94
95-th percentile392.797
Maximum16870.98
Range16871.78
Interquartile range (IQR)84.445

Descriptive statistics

Standard deviation538.83223
Coefficient of variation (CV)4.5006211
Kurtosis474.89222
Mean119.72397
Median Absolute Deviation (MAD)27.94
Skewness19.481875
Sum403110.61
Variance290340.17
MonotonicityNot monotonic
2023-06-09T10:25:44.788736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9
 
0.3%
3.95 7
 
0.2%
4.2 6
 
0.2%
5.46 6
 
0.2%
4.74 5
 
0.1%
5.07 5
 
0.1%
6.18 5
 
0.1%
3.63 5
 
0.1%
4.8 5
 
0.1%
4.02 5
 
0.1%
Other values (2808) 3309
98.0%
(Missing) 9
 
0.3%
ValueCountFrequency (%)
-0.8 1
 
< 0.1%
0 9
0.3%
0.09 1
 
< 0.1%
0.12 1
 
< 0.1%
0.17 1
 
< 0.1%
0.31 1
 
< 0.1%
0.4 1
 
< 0.1%
0.5 1
 
< 0.1%
0.63 1
 
< 0.1%
0.68 1
 
< 0.1%
ValueCountFrequency (%)
16870.98 1
< 0.1%
12307.16 1
< 0.1%
11140.56 1
< 0.1%
10734.57 1
< 0.1%
8145.52 1
< 0.1%
6330.91 1
< 0.1%
4906.33 1
< 0.1%
3995.45 1
< 0.1%
3768.66 1
< 0.1%
3278.11 1
< 0.1%

GHGEmissionsIntensity
Real number (ℝ)

Distinct511
Distinct (%)15.2%
Missing9
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean1.1759162
Minimum-0.02
Maximum34.09
Zeros12
Zeros (%)0.4%
Negative1
Negative (%)< 0.1%
Memory size26.5 KiB
2023-06-09T10:25:45.296472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.02
5-th percentile0.13
Q10.21
median0.61
Q31.37
95-th percentile3.961
Maximum34.09
Range34.11
Interquartile range (IQR)1.16

Descriptive statistics

Standard deviation1.8214518
Coefficient of variation (CV)1.5489639
Kurtosis57.372156
Mean1.1759162
Median Absolute Deviation (MAD)0.44
Skewness5.5931448
Sum3959.31
Variance3.3176866
MonotonicityNot monotonic
2023-06-09T10:25:45.784821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.17 99
 
2.9%
0.15 99
 
2.9%
0.16 96
 
2.8%
0.18 86
 
2.5%
0.19 78
 
2.3%
0.2 70
 
2.1%
0.13 66
 
2.0%
0.14 62
 
1.8%
0.21 60
 
1.8%
0.22 54
 
1.6%
Other values (501) 2597
76.9%
ValueCountFrequency (%)
-0.02 1
 
< 0.1%
0 12
0.4%
0.01 4
 
0.1%
0.02 4
 
0.1%
0.03 7
0.2%
0.04 9
0.3%
0.05 9
0.3%
0.06 16
0.5%
0.07 8
0.2%
0.08 8
0.2%
ValueCountFrequency (%)
34.09 1
< 0.1%
25.71 1
< 0.1%
16.99 1
< 0.1%
16.93 1
< 0.1%
16.91 1
< 0.1%
16.38 1
< 0.1%
15.42 1
< 0.1%
14.94 1
< 0.1%
14.89 1
< 0.1%
14.32 1
< 0.1%

Interactions

2023-06-09T10:25:03.892401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:22:20.079339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:22:28.912808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:22:38.140878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:22:48.791354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:22:56.755679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:04.729210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:12.697015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:22.924841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:31.793864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:39.319813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:47.995986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:56.401959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:05.742036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:15.320845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:24.313573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:36.118568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:45.763319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:55.124015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:25:04.353958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:22:20.575811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:22:29.313023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:22:38.569025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:22:49.430451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:22:57.219195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:05.190973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:13.117371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:23.346370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:32.203751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:39.840939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:48.387601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:56.825873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:06.156710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:15.782668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-06-09T10:23:01.516429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:09.908572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:19.986841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:28.622824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:36.453059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:44.819142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:53.284113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:02.759454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:12.229214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:20.910788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:31.715538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:42.618576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:51.092608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:25:00.738303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:25:10.320116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:22:26.332918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:22:34.970859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:22:45.244712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:22:54.017016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:01.938367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:10.285415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:20.379315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:29.022735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:36.815009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:45.241431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:53.712119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:03.133684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:12.614683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:21.323607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:32.285294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:43.025879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:51.487494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:25:01.158644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:25:10.810996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:22:26.760504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:22:35.454937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:22:45.858008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:22:54.443081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:02.370938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:10.668061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:20.786605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:29.437719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:37.170134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:45.630756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:54.116820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:03.499320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:13.083460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:21.809994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:32.881951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:43.414679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:51.939292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:25:01.565223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:25:11.295571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:22:27.198653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:22:36.061721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:22:46.472332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:22:54.914352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:02.850531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:11.078289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:21.241132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:29.918964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:37.571944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:46.147851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:54.544200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:03.924787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:13.519029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:22.335123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:33.556108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:43.893566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:53.327596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:25:02.004064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:25:11.748926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:22:27.628654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:22:36.613371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:22:47.098624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:22:55.370896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:03.343953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:11.471128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:21.676527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:30.375985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:37.973069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:46.607007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:55.055201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:04.302093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:13.950897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:22.919396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:34.150273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:44.295113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:53.805994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:25:02.450724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:25:12.201280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:22:28.021934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:22:37.184077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:22:47.662979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:22:55.821515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:03.795218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:11.858731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:22.072921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:30.850260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:38.408294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:47.067426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:55.511362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:04.798119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:14.376693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:23.460290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:34.755145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:44.718942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:54.285194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:25:02.916087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:25:12.677518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:22:28.469438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:22:37.676349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:22:48.134902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:22:56.281457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:04.256816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:12.268487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:22.499591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:31.339495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:38.856394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:47.602003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:23:55.955142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:05.263844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:14.869772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:23.892660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:35.382012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:45.244676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:24:54.706655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-09T10:25:03.397894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-09T10:25:46.326139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ZipCodeYearBuiltNumberofBuildingsNumberofFloorsPropertyGFATotalPropertyGFAParkingPropertyGFABuilding(s)ENERGYSTARScoreSiteEUI(kBtu/sf)SiteEUIWN(kBtu/sf)SourceEUI(kBtu/sf)SourceEUIWN(kBtu/sf)SiteEnergyUse(kBtu)SiteEnergyUseWN(kBtu)SteamUse(kBtu)Electricity(kBtu)NaturalGas(kBtu)TotalGHGEmissionsGHGEmissionsIntensityBuildingTypePrimaryPropertyTypeNeighborhoodComplianceStatusOutlier
ZipCode1.0000.0890.014-0.228-0.089-0.126-0.076-0.001-0.132-0.128-0.108-0.108-0.119-0.116-0.202-0.084-0.047-0.126-0.0920.0530.0780.2540.0370.000
YearBuilt0.0891.0000.0350.2920.3120.2420.2830.075-0.060-0.0830.0630.0420.1590.147-0.1360.303-0.0160.027-0.1960.1580.1860.1760.0590.446
NumberofBuildings0.0140.0351.000-0.0230.0690.0060.0680.0370.0070.000-0.003-0.0090.0570.0500.0180.0490.0330.0490.0160.2400.1360.0140.0001.000
NumberofFloors-0.2280.292-0.0231.0000.4400.2620.4310.1180.0270.0020.0990.0850.2900.2750.1600.3480.0070.175-0.1020.2620.2760.1400.0000.000
PropertyGFATotal-0.0890.3120.0690.4401.0000.3450.9830.0740.1890.1630.2110.1870.7530.7370.1710.7680.3000.5790.0680.1440.1730.0570.0001.000
PropertyGFAParking-0.1260.2420.0060.2620.3451.0000.2210.0100.1990.1790.2470.2300.3040.2910.0590.3290.0540.208-0.0090.0520.1550.0580.0001.000
PropertyGFABuilding(s)-0.0760.2830.0680.4310.9830.2211.0000.0740.1650.1410.1800.1580.7380.7230.1740.7480.3050.5740.0740.1660.1900.0470.0001.000
ENERGYSTARScore-0.0010.0750.0370.1180.0740.0100.0741.000-0.459-0.460-0.526-0.536-0.191-0.191-0.032-0.197-0.056-0.118-0.2370.1150.1180.0540.1200.976
SiteEUI(kBtu/sf)-0.132-0.0600.0070.0270.1890.1990.165-0.4591.0000.9870.8720.8690.7140.7060.1780.5020.5250.7130.7590.1330.2750.0540.0320.966
SiteEUIWN(kBtu/sf)-0.128-0.0830.0000.0020.1630.1790.141-0.4600.9871.0000.8420.8640.6920.7090.1650.4650.5430.7090.7740.1330.2670.0510.0300.966
SourceEUI(kBtu/sf)-0.1080.063-0.0030.0990.2110.2470.180-0.5260.8720.8421.0000.9860.6430.6250.1600.6600.2160.4740.4390.1110.2410.0170.0560.527
SourceEUIWN(kBtu/sf)-0.1080.042-0.0090.0850.1870.2300.158-0.5360.8690.8640.9861.0000.6250.6330.1470.6310.2250.4670.4470.1140.2530.0000.0520.527
SiteEnergyUse(kBtu)-0.1190.1590.0570.2900.7530.3040.738-0.1910.7140.6920.6430.6251.0000.9860.2160.8620.5690.8750.5560.1560.2770.0000.0001.000
SiteEnergyUseWN(kBtu)-0.1160.1470.0500.2750.7370.2910.723-0.1910.7060.7090.6250.6330.9861.0000.2030.8370.5840.8730.5650.1390.2980.0360.0001.000
SteamUse(kBtu)-0.202-0.1360.0180.1600.1710.0590.174-0.0320.1780.1650.1600.1470.2160.2031.0000.177-0.0140.2550.2080.0850.2820.0000.0001.000
Electricity(kBtu)-0.0840.3030.0490.3480.7680.3290.748-0.1970.5020.4650.6600.6310.8620.8370.1771.0000.2230.5730.1810.1120.2380.0000.0001.000
NaturalGas(kBtu)-0.047-0.0160.0330.0070.3000.0540.305-0.0560.5250.5430.2160.2250.5690.584-0.0140.2231.0000.8310.8150.1550.2590.0000.0001.000
TotalGHGEmissions-0.1260.0270.0490.1750.5790.2080.574-0.1180.7130.7090.4740.4670.8750.8730.2550.5730.8311.0000.8240.1260.2590.0000.0001.000
GHGEmissionsIntensity-0.092-0.1960.016-0.1020.068-0.0090.074-0.2370.7590.7740.4390.4470.5560.5650.2080.1810.8150.8241.0000.1260.2680.0000.0080.624
BuildingType0.0530.1580.2400.2620.1440.0520.1660.1150.1330.1330.1110.1140.1560.1390.0850.1120.1550.1260.1261.0000.7320.2030.4510.175
PrimaryPropertyType0.0780.1860.1360.2760.1730.1550.1900.1180.2750.2670.2410.2530.2770.2980.2820.2380.2590.2590.2680.7321.0000.2340.3810.233
Neighborhood0.2540.1760.0140.1400.0570.0580.0470.0540.0540.0510.0170.0000.0000.0360.0000.0000.0000.0000.0000.2030.2341.0000.0980.393
ComplianceStatus0.0370.0590.0000.0000.0000.0000.0000.1200.0320.0300.0560.0520.0000.0000.0000.0000.0000.0000.0080.4510.3810.0981.0000.000
Outlier0.0000.4461.0000.0001.0001.0001.0000.9760.9660.9660.5270.5271.0001.0001.0001.0001.0001.0000.6240.1750.2330.3930.0001.000

Missing values

2023-06-09T10:25:13.420502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-09T10:25:15.197092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-09T10:25:16.828608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

BuildingTypePrimaryPropertyTypeZipCodeNeighborhoodYearBuiltNumberofBuildingsNumberofFloorsPropertyGFATotalPropertyGFAParkingPropertyGFABuilding(s)ENERGYSTARScoreSiteEUI(kBtu/sf)SiteEUIWN(kBtu/sf)SourceEUI(kBtu/sf)SourceEUIWN(kBtu/sf)SiteEnergyUse(kBtu)SiteEnergyUseWN(kBtu)SteamUse(kBtu)Electricity(kBtu)NaturalGas(kBtu)ComplianceStatusOutlierTotalGHGEmissionsGHGEmissionsIntensity
0nonresidentialhotel98101.0downtown19271.0128843408843460.081.69999784.300003182.500000189.0000007226362.57456910.02.003882e+063946027.01276453.0compliantNaN249.982.83
1nonresidentialhotel98101.0downtown19961.011103566150648850261.094.80000397.900002176.100006179.3999948387933.08664479.00.000000e+003242851.05145082.0compliantNaN295.862.86
2nonresidentialhotel98101.0downtown19691.04195611019671875939243.096.00000097.699997241.899994244.10000672587024.073937112.02.156655e+0749526664.01493800.0compliantNaN2089.282.19
3nonresidentialhotel98101.0downtown19261.0106132006132056.0110.800003113.300003216.199997224.0000006794584.06946800.52.214446e+062768924.01811213.0compliantNaN286.434.67
4nonresidentialhotel98121.0downtown19801.0181755806200011358075.0114.800003118.699997211.399994215.60000614172606.014656503.00.000000e+005368607.08803998.0compliantNaN505.012.88
5nonresidential cosother98101.0downtown19991.02972883719860090NaN136.100006141.600006316.299988320.50000012086616.012581712.00.000000e+007371434.04715182.0compliantNaN301.813.10
6nonresidentialhotel98101.0downtown19261.0118300808300827.070.80000374.500000146.600006154.6999975758795.06062767.50.000000e+002811215.02947580.0compliantNaN176.142.12
7nonresidentialother98101.0downtown19261.081027610102761NaN61.29999968.800003141.699997152.3000036298131.57067881.52.276286e+063636655.0385189.0compliantNaN221.512.16
8nonresidentialhotel98104.0downtown19041.015163984016398443.083.69999786.599998180.899994187.19999713723820.014194054.00.000000e+007297919.06425900.0compliantNaN392.162.39
9multifamily mr (5-9)mid-rise multifamily98104.0downtown19101.06637121496622161.081.50000085.599998182.699997187.3999944573777.04807679.51.039735e+062532015.01002026.0compliantNaN151.122.37
BuildingTypePrimaryPropertyTypeZipCodeNeighborhoodYearBuiltNumberofBuildingsNumberofFloorsPropertyGFATotalPropertyGFAParkingPropertyGFABuilding(s)ENERGYSTARScoreSiteEUI(kBtu/sf)SiteEUIWN(kBtu/sf)SourceEUI(kBtu/sf)SourceEUIWN(kBtu/sf)SiteEnergyUse(kBtu)SiteEnergyUseWN(kBtu)SteamUse(kBtu)Electricity(kBtu)NaturalGas(kBtu)ComplianceStatusOutlierTotalGHGEmissionsGHGEmissionsIntensity
3366nonresidential cosofficeNaNmagnolia / queen anne19521.011366101366175.036.79999940.900002115.500000128.3999945.026677e+055.585251e+050.05.026678e+050.000000e+00error - correct default dataNaN3.500.26
3367nonresidential cosotherNaNeast19121.0123445023445NaN254.899994286.500000380.100006413.2000125.976246e+066.716330e+060.01.260870e+064.715376e+06compliantNaN259.2211.06
3368nonresidential cosmixed use propertyNaNcentral19941.0120050020050NaN90.40000299.400002175.199997184.6000061.813404e+061.993137e+060.07.694531e+051.043951e+06compliantNaN60.813.03
3369nonresidential cosofficeNaNsoutheast19601.011539801539893.025.20000126.90000064.09999866.6999973.878100e+054.141724e+050.02.775369e+051.102730e+05error - correct default dataNaN7.790.51
3370nonresidential cosotherNaNdelridge neighborhoods19821.0118261018261NaN51.00000056.200001126.000000136.6000069.320821e+051.025432e+060.06.323620e+052.997200e+05compliantNaN20.331.11
3371nonresidential cosofficeNaNgreater duwamish19901.011229401229446.069.09999876.699997161.699997176.1000068.497457e+059.430032e+050.05.242709e+053.254750e+05error - correct default dataNaN20.941.70
3372nonresidential cosotherNaNdowntown20041.0116000016000NaN59.40000265.900002114.199997118.9000029.502762e+051.053706e+060.03.965461e+055.537300e+05compliantNaN32.172.01
3373nonresidential cosotherNaNmagnolia / queen anne19741.0113157013157NaN438.200012460.100006744.799988767.7999885.765898e+066.053764e+060.01.792159e+063.973739e+06compliantNaN223.5416.99
3374nonresidential cosmixed use propertyNaNgreater duwamish19891.0114101014101NaN51.00000055.500000105.300003110.8000037.194712e+057.828413e+050.03.488702e+053.706010e+05compliantNaN22.111.57
3375nonresidential cosmixed use propertyNaNgreater duwamish19381.0118258018258NaN63.09999870.900002115.800003123.9000021.152896e+061.293722e+060.04.325542e+057.203420e+05compliantNaN41.272.26